The Electro-Cardiogram or ECG has long provided a means for monitoring and diagnosing patient conditions. Traditionally, ECGs have been recorded by attaching two or more electrodes to a patient's chest, performing some signal processing on the recorded signal such as filtering to remove noise, and recording the processed result. Considering the case of a patient with a suspected heart condition, one way to diagnose the condition is to continuously record ECG data for some period of time, e.g. several hours, and for a physician to study the recorded data to identify any anomalies. This is inconvenient for the patient as the data collection systems tend to be large and therefore the patient must remain in the same location for the duration of the process, and is time consuming on the part of the physician.
To overcome these problems, relatively sophisticated signal processing techniques have been developed to analyse substantially real time ECG data to classify heart beats as either normal or abnormal, and to record only the occurrence and type of an abnormal heart beat. As the data storage requirements are massively reduced—only event and type data rather than continuous signals need to be recorded—the recording apparatus can be made portable and even wearable, whilst physicians are presented with a simple classification of heart beat events which makes diagnosis easier and quicker.
It is known, for example, to provide a wearable computer device which may be around the size of a pack of cards, and which is coupled to a pair of chest worn electrodes. The computer device compares recorded heartbeats against a number of stored “template heartbeats” and uses the results to classify the recorded heartbeats. An ongoing count is maintained of the heartbeats matching each template. Heartbeats that do not match any template may be stored for future analysis. However, as these unmatched templates can be expected to occur only infrequently, the data storage requirements are not excessive. The heartbeat classification process is computationally intensive and therefore consumes a relatively large amount of power. However, this power requirement can generally be provided by a battery located within the computer device.
Whilst representing a considerable advance over systems of the type that require a patient to remain at a particular location, portable monitoring systems of this type are still relatively bulky. One approach to making the monitoring systems more user friendly might be to configure them as two part systems, with a first relatively small and wearable part being coupled to the monitoring electrodes and transmitting the monitored ECG signal over a wireless link to a second, larger data processing device (which may be carried in a pocket or may be left elsewhere within the same locality, i.e. within range of the worn part). However, the power required to allow continuous broadcast of ECG signals will place a limit both on the size of the wearable part and on the battery lifetime. Furthermore, if the wearer moves out of range of the data processing device, data will be lost.
It will be appreciated that analogous problems will arise with systems for monitoring other physiological “conditions” such as electro-encephelogrammes (EEGs), blood glucose levels, etc (where bioelectrical transducers such as biosensors are used to convert a biochemical parameter into an electrical signal). A solution to these problems may also find applications in non-medical areas such as industrial process control.